Manager I, Data Scientist

About the Role

Kroll is seeking a Data Science Manager to lead and expand the data science function within our Enterprise Data Group. This position combines technical leadership with strategic direction, where you will determine how data science is implemented across the team, oversee the roadmap for machine learning (ML) and artificial intelligence (AI) projects, and develop the data science team responsible for executing these initiatives.

Key Responsibilities

• Guide and mentor a diverse team of data scientists at various experience levels, fostering technical growth and a high-achieving team environment.
• Manage the complete data science project lifecycle by prioritizing tasks, ensuring timely delivery, and reporting progress and outcomes to senior leadership and clients.
• Collaborate closely with product, engineering, and business teams to define project goals, scope ML solutions, and convert data science efforts into concrete business results.
• Oversee all stages of ML development including problem formulation, data preparation, model creation, experimentation, deployment, and continuous monitoring.
• Implement and maintain standards related to code quality, experimental rigor, model governance, and ethical AI practices.
• Promote and advance ML infrastructure leveraging platforms like Databricks and Azure (Azure AI Foundry, Azure OpenAI, AKS), implementing MLOps methodologies such as continuous integration and deployment (CI/CD), version control, and anomaly detection.
• Lead initiatives involving large language models (LLMs) and generative AI including retrieval-augmented generation (RAG) architecture, prompt design, fine-tuning methods, and autonomous agent frameworks, ensuring responsible evaluation and deployment.
• Drive recruitment, onboarding, and performance management to attract and retain skilled data science professionals.
• Represent data science initiatives internally and externally, effectively communicating technical concepts and decisions to both specialist and non-specialist audiences.

Required Qualifications and Experience

• Advanced degree (Master's or PhD) in computer science, statistics, mathematics, data science, or a related quantitative discipline.
• At least 7 years of hands-on experience in applied data science or machine learning, with a minimum of 2 years in managerial or technical leadership roles.
• Demonstrated success delivering ML solutions into production environments that generate measurable business benefits.
• Strong proficiency with Python and familiarity with modern ML frameworks such as scikit-learn, PyTorch or TensorFlow, Hugging Face Transformers, and pandas.
• Practical experience using Databricks tools including notebooks, jobs, MLflow, and Unity Catalog, alongside Spark/PySpark skills.
• Experience with Microsoft Azure cloud services, preferably Azure AI Foundry, Azure OpenAI Service, and Azure Data Lake.
• Comprehensive knowledge across ML subfields: traditional statistical ML, deep learning, natural language processing (NLP), and expertise in LLM and generative AI including prompt engineering, RAG architectures, embedding techniques, and agentic workflows.
• Established experience setting up MLOps best practices such as CI/CD pipelines, model health monitoring, concept drift detection, and version control.
• Excellent communication abilities to distill complex data science topics into clear, actionable insights suited to leadership and client audiences.
• Sound judgment in balancing priorities, handling trade-offs, and managing diverse stakeholder expectations.

Preferred Skills and Experience

• Background in financial services, particularly relating to risk management, compliance, or regulatory frameworks.
• Hands-on work with agentic AI platforms like LangChain, LlamaIndex, Semantic Kernel, as well as LLM evaluation tools and generative AI production deployments.
• Familiarity with principles of responsible AI encompassing fairness, explainability, and privacy safeguards.
• Experience with containerization and orchestration tools such as Docker and Kubernetes, plus CI/CD tooling like Azure DevOps or GitHub Actions.